Overview

Dataset statistics

Number of variables37
Number of observations9240
Missing cells41039
Missing cells (%)12.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.6 MiB
Average record size in memory296.0 B

Variable types

Categorical17
Numeric6
Boolean14

Alerts

Magazine has constant value "False" Constant
Receive More Updates About Our Courses has constant value "False" Constant
Update me on Supply Chain Content has constant value "False" Constant
Get updates on DM Content has constant value "False" Constant
I agree to pay the amount through cheque has constant value "False" Constant
Prospect ID has a high cardinality: 9240 distinct values High cardinality
TotalVisits is highly correlated with Total Time Spent on Website and 1 other fieldsHigh correlation
Total Time Spent on Website is highly correlated with TotalVisits and 1 other fieldsHigh correlation
Page Views Per Visit is highly correlated with TotalVisits and 1 other fieldsHigh correlation
TotalVisits is highly correlated with Page Views Per VisitHigh correlation
Page Views Per Visit is highly correlated with TotalVisitsHigh correlation
TotalVisits is highly correlated with Page Views Per VisitHigh correlation
Page Views Per Visit is highly correlated with TotalVisitsHigh correlation
Through Recommendations is highly correlated with Magazine and 4 other fieldsHigh correlation
Magazine is highly correlated with Through Recommendations and 28 other fieldsHigh correlation
Tags is highly correlated with Magazine and 7 other fieldsHigh correlation
Lead Profile is highly correlated with Magazine and 5 other fieldsHigh correlation
Do Not Call is highly correlated with Magazine and 4 other fieldsHigh correlation
City is highly correlated with Magazine and 5 other fieldsHigh correlation
Converted is highly correlated with Magazine and 6 other fieldsHigh correlation
Lead Origin is highly correlated with Magazine and 6 other fieldsHigh correlation
Lead Quality is highly correlated with Magazine and 7 other fieldsHigh correlation
Newspaper Article is highly correlated with Magazine and 4 other fieldsHigh correlation
Digital Advertisement is highly correlated with Magazine and 4 other fieldsHigh correlation
Last Notable Activity is highly correlated with Magazine and 5 other fieldsHigh correlation
What matters most to you in choosing a course is highly correlated with Magazine and 5 other fieldsHigh correlation
Country is highly correlated with Magazine and 4 other fieldsHigh correlation
How did you hear about X Education is highly correlated with Magazine and 4 other fieldsHigh correlation
Lead Source is highly correlated with Magazine and 6 other fieldsHigh correlation
I agree to pay the amount through cheque is highly correlated with Through Recommendations and 28 other fieldsHigh correlation
What is your current occupation is highly correlated with Magazine and 5 other fieldsHigh correlation
Get updates on DM Content is highly correlated with Through Recommendations and 28 other fieldsHigh correlation
Do Not Email is highly correlated with Magazine and 5 other fieldsHigh correlation
Asymmetrique Activity Index is highly correlated with Magazine and 6 other fieldsHigh correlation
X Education Forums is highly correlated with Magazine and 11 other fieldsHigh correlation
Last Activity is highly correlated with Magazine and 6 other fieldsHigh correlation
Update me on Supply Chain Content is highly correlated with Through Recommendations and 28 other fieldsHigh correlation
Asymmetrique Profile Index is highly correlated with Magazine and 6 other fieldsHigh correlation
Specialization is highly correlated with Magazine and 4 other fieldsHigh correlation
Receive More Updates About Our Courses is highly correlated with Through Recommendations and 28 other fieldsHigh correlation
A free copy of Mastering The Interview is highly correlated with Magazine and 7 other fieldsHigh correlation
Newspaper is highly correlated with Magazine and 6 other fieldsHigh correlation
Search is highly correlated with Magazine and 4 other fieldsHigh correlation
Lead Origin is highly correlated with Lead Source and 4 other fieldsHigh correlation
Lead Source is highly correlated with Lead Origin and 5 other fieldsHigh correlation
Do Not Email is highly correlated with Last Activity and 1 other fieldsHigh correlation
Do Not Call is highly correlated with X Education Forums and 1 other fieldsHigh correlation
Converted is highly correlated with Total Time Spent on Website and 4 other fieldsHigh correlation
Total Time Spent on Website is highly correlated with ConvertedHigh correlation
Last Activity is highly correlated with Do Not Email and 2 other fieldsHigh correlation
Specialization is highly correlated with Lead Origin and 4 other fieldsHigh correlation
Newspaper Article is highly correlated with X Education Forums and 1 other fieldsHigh correlation
X Education Forums is highly correlated with Do Not Call and 2 other fieldsHigh correlation
Newspaper is highly correlated with Do Not Call and 2 other fieldsHigh correlation
Tags is highly correlated with Converted and 4 other fieldsHigh correlation
Lead Quality is highly correlated with Converted and 2 other fieldsHigh correlation
Lead Profile is highly correlated with Converted and 3 other fieldsHigh correlation
City is highly correlated with Lead Origin and 4 other fieldsHigh correlation
Asymmetrique Activity Index is highly correlated with Lead Source and 2 other fieldsHigh correlation
Asymmetrique Profile Index is highly correlated with Lead Origin and 4 other fieldsHigh correlation
Asymmetrique Activity Score is highly correlated with Converted and 3 other fieldsHigh correlation
Asymmetrique Profile Score is highly correlated with Lead Origin and 5 other fieldsHigh correlation
A free copy of Mastering The Interview is highly correlated with Lead Source and 1 other fieldsHigh correlation
Last Notable Activity is highly correlated with Do Not Email and 1 other fieldsHigh correlation
TotalVisits has 137 (1.5%) missing values Missing
Page Views Per Visit has 137 (1.5%) missing values Missing
Last Activity has 103 (1.1%) missing values Missing
Country has 2461 (26.6%) missing values Missing
Specialization has 1438 (15.6%) missing values Missing
How did you hear about X Education has 2207 (23.9%) missing values Missing
What is your current occupation has 2690 (29.1%) missing values Missing
What matters most to you in choosing a course has 2709 (29.3%) missing values Missing
Tags has 3353 (36.3%) missing values Missing
Lead Quality has 4767 (51.6%) missing values Missing
Lead Profile has 2709 (29.3%) missing values Missing
City has 1420 (15.4%) missing values Missing
Asymmetrique Activity Index has 4218 (45.6%) missing values Missing
Asymmetrique Profile Index has 4218 (45.6%) missing values Missing
Asymmetrique Activity Score has 4218 (45.6%) missing values Missing
Asymmetrique Profile Score has 4218 (45.6%) missing values Missing
Prospect ID is uniformly distributed Uniform
Prospect ID has unique values Unique
Lead Number has unique values Unique
TotalVisits has 2189 (23.7%) zeros Zeros
Total Time Spent on Website has 2193 (23.7%) zeros Zeros
Page Views Per Visit has 2189 (23.7%) zeros Zeros

Reproduction

Analysis started2021-10-28 13:45:17.295956
Analysis finished2021-10-28 13:45:59.909111
Duration42.61 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

Prospect ID
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct9240
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size72.3 KiB
d5312a24-2330-4d26-aa99-41816f77b03a
 
1
8c2d6237-f8bf-4fd5-a98a-951bc13d0312
 
1
1e765aa6-2eb0-47bf-be58-0f9f8b3a312d
 
1
4c571fb2-3603-4266-b644-d14bcc3951e8
 
1
88f96e3a-3885-450e-86e3-015ccb7ae528
 
1
Other values (9235)
9235 

Length

Max length36
Median length36
Mean length36
Min length36

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9240 ?
Unique (%)100.0%

Sample

1st row7927b2df-8bba-4d29-b9a2-b6e0beafe620
2nd row2a272436-5132-4136-86fa-dcc88c88f482
3rd row8cc8c611-a219-4f35-ad23-fdfd2656bd8a
4th row0cc2df48-7cf4-4e39-9de9-19797f9b38cc
5th row3256f628-e534-4826-9d63-4a8b88782852

Common Values

ValueCountFrequency (%)
d5312a24-2330-4d26-aa99-41816f77b03a1
 
< 0.1%
8c2d6237-f8bf-4fd5-a98a-951bc13d03121
 
< 0.1%
1e765aa6-2eb0-47bf-be58-0f9f8b3a312d1
 
< 0.1%
4c571fb2-3603-4266-b644-d14bcc3951e81
 
< 0.1%
88f96e3a-3885-450e-86e3-015ccb7ae5281
 
< 0.1%
c7140518-2ee5-446f-a6be-a2d5a09835f31
 
< 0.1%
0287aa77-3154-4565-9970-9e4fa7fc63ba1
 
< 0.1%
2f9dac66-52ae-4c93-a955-a37b6232c6ea1
 
< 0.1%
f46bc81e-2d43-4252-9d1b-d55a896e8c141
 
< 0.1%
e7160ec2-b004-446f-b7bd-346c684a4d311
 
< 0.1%
Other values (9230)9230
99.9%

Length

2021-10-28T19:16:00.102924image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
d5312a24-2330-4d26-aa99-41816f77b03a1
 
< 0.1%
75371b3e-4821-457c-b140-e14c8c64b07a1
 
< 0.1%
39fd64c1-3df6-43e4-9ff0-7ab5f80110091
 
< 0.1%
077bc905-2dcb-4ecb-b481-e9adc787e0bd1
 
< 0.1%
c0282dcb-b7c5-42f3-9a33-09c099f178121
 
< 0.1%
dd7f46e0-fb8c-4eaf-81a1-fdcf41b497681
 
< 0.1%
45d86ce2-351b-40cb-8a77-ddf5b540b36c1
 
< 0.1%
c59054f4-d4f6-4e10-b472-fbf3cacce01c1
 
< 0.1%
70ea83e1-de7d-4e52-931b-6127021fc4e41
 
< 0.1%
dcff3ee2-1519-4a58-81d7-cf130b358e551
 
< 0.1%
Other values (9230)9230
99.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Lead Number
Real number (ℝ≥0)

UNIQUE

Distinct9240
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean617188.4356
Minimum579533
Maximum660737
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size72.3 KiB
2021-10-28T19:16:00.399800image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum579533
5-th percentile582869.9
Q1596484.5
median615479
Q3637387.25
95-th percentile655404.05
Maximum660737
Range81204
Interquartile range (IQR)40902.75

Descriptive statistics

Standard deviation23405.9957
Coefficient of variation (CV)0.03792358111
Kurtosis-1.206393328
Mean617188.4356
Median Absolute Deviation (MAD)20413.5
Skewness0.1404510858
Sum5702821145
Variance547840634.6
MonotonicityNot monotonic
2021-10-28T19:16:00.696637image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6553601
 
< 0.1%
6046981
 
< 0.1%
6212421
 
< 0.1%
6191951
 
< 0.1%
6007681
 
< 0.1%
6314891
 
< 0.1%
6540181
 
< 0.1%
6355871
 
< 0.1%
6396851
 
< 0.1%
6099511
 
< 0.1%
Other values (9230)9230
99.9%
ValueCountFrequency (%)
5795331
< 0.1%
5795381
< 0.1%
5795451
< 0.1%
5795461
< 0.1%
5795641
< 0.1%
5796151
< 0.1%
5796221
< 0.1%
5796421
< 0.1%
5796971
< 0.1%
5797011
< 0.1%
ValueCountFrequency (%)
6607371
< 0.1%
6607281
< 0.1%
6607271
< 0.1%
6607191
< 0.1%
6606811
< 0.1%
6606801
< 0.1%
6606731
< 0.1%
6606641
< 0.1%
6606241
< 0.1%
6606161
< 0.1%

Lead Origin
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size72.3 KiB
Landing Page Submission
4886 
API
3580 
Lead Add Form
718 
Lead Import
 
55
Quick Add Form
 
1

Length

Max length23
Median length23
Mean length14.40162338
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowAPI
2nd rowAPI
3rd rowLanding Page Submission
4th rowLanding Page Submission
5th rowLanding Page Submission

Common Values

ValueCountFrequency (%)
Landing Page Submission4886
52.9%
API3580
38.7%
Lead Add Form718
 
7.8%
Lead Import55
 
0.6%
Quick Add Form1
 
< 0.1%

Length

2021-10-28T19:16:01.009130image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-28T19:16:01.180979image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
landing4886
23.8%
page4886
23.8%
submission4886
23.8%
api3580
17.5%
lead773
 
3.8%
add719
 
3.5%
form719
 
3.5%
import55
 
0.3%
quick1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Lead Source
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct21
Distinct (%)0.2%
Missing36
Missing (%)0.4%
Memory size72.3 KiB
Google
2868 
Direct Traffic
2543 
Olark Chat
1755 
Organic Search
1154 
Reference
534 
Other values (16)
350 

Length

Max length17
Median length10
Mean length10.43209474
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)0.1%

Sample

1st rowOlark Chat
2nd rowOrganic Search
3rd rowDirect Traffic
4th rowDirect Traffic
5th rowGoogle

Common Values

ValueCountFrequency (%)
Google2868
31.0%
Direct Traffic2543
27.5%
Olark Chat1755
19.0%
Organic Search1154
12.5%
Reference534
 
5.8%
Welingak Website142
 
1.5%
Referral Sites125
 
1.4%
Facebook55
 
0.6%
bing6
 
0.1%
google5
 
0.1%
Other values (11)17
 
0.2%
(Missing)36
 
0.4%

Length

2021-10-28T19:16:01.524704image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
google2873
19.2%
direct2543
17.0%
traffic2543
17.0%
chat1757
11.8%
olark1755
11.8%
organic1154
7.7%
search1154
7.7%
reference534
 
3.6%
welingak142
 
1.0%
website142
 
1.0%
Other values (19)333
 
2.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Do Not Email
Boolean

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.1 KiB
False
8506 
True
 
734
ValueCountFrequency (%)
False8506
92.1%
True734
 
7.9%
2021-10-28T19:16:01.712206image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Do Not Call
Boolean

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.1 KiB
False
9238 
True
 
2
ValueCountFrequency (%)
False9238
> 99.9%
True2
 
< 0.1%
2021-10-28T19:16:01.805949image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Converted
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size72.3 KiB
0
5679 
1
3561 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
05679
61.5%
13561
38.5%

Length

2021-10-28T19:16:01.962188image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-28T19:16:02.087178image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
05679
61.5%
13561
38.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

TotalVisits
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct41
Distinct (%)0.5%
Missing137
Missing (%)1.5%
Infinite0
Infinite (%)0.0%
Mean3.445237834
Minimum0
Maximum251
Zeros2189
Zeros (%)23.7%
Negative0
Negative (%)0.0%
Memory size72.3 KiB
2021-10-28T19:16:02.243420image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q35
95-th percentile10
Maximum251
Range251
Interquartile range (IQR)4

Descriptive statistics

Standard deviation4.854852697
Coefficient of variation (CV)1.409148782
Kurtosis853.478706
Mean3.445237834
Median Absolute Deviation (MAD)2
Skewness19.91165734
Sum31362
Variance23.56959471
MonotonicityNot monotonic
2021-10-28T19:16:02.587122image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
02189
23.7%
21680
18.2%
31306
14.1%
41120
12.1%
5783
 
8.5%
6466
 
5.0%
1395
 
4.3%
7309
 
3.3%
8224
 
2.4%
9164
 
1.8%
Other values (31)467
 
5.1%
(Missing)137
 
1.5%
ValueCountFrequency (%)
02189
23.7%
1395
 
4.3%
21680
18.2%
31306
14.1%
41120
12.1%
5783
 
8.5%
6466
 
5.0%
7309
 
3.3%
8224
 
2.4%
9164
 
1.8%
ValueCountFrequency (%)
2511
< 0.1%
1411
< 0.1%
1151
< 0.1%
741
< 0.1%
551
< 0.1%
541
< 0.1%
431
< 0.1%
421
< 0.1%
411
< 0.1%
321
< 0.1%

Total Time Spent on Website
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct1731
Distinct (%)18.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean487.6982684
Minimum0
Maximum2272
Zeros2193
Zeros (%)23.7%
Negative0
Negative (%)0.0%
Memory size72.3 KiB
2021-10-28T19:16:02.868353image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q112
median248
Q3936
95-th percentile1562
Maximum2272
Range2272
Interquartile range (IQR)924

Descriptive statistics

Standard deviation548.0214663
Coefficient of variation (CV)1.123689588
Kurtosis-0.4037697308
Mean487.6982684
Median Absolute Deviation (MAD)248
Skewness0.956450193
Sum4506332
Variance300327.5275
MonotonicityNot monotonic
2021-10-28T19:16:03.102730image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02193
 
23.7%
6019
 
0.2%
12718
 
0.2%
7518
 
0.2%
7418
 
0.2%
8717
 
0.2%
3217
 
0.2%
23417
 
0.2%
15717
 
0.2%
6217
 
0.2%
Other values (1721)6889
74.6%
ValueCountFrequency (%)
02193
23.7%
17
 
0.1%
214
 
0.2%
39
 
0.1%
410
 
0.1%
513
 
0.1%
67
 
0.1%
78
 
0.1%
811
 
0.1%
911
 
0.1%
ValueCountFrequency (%)
22721
< 0.1%
22531
< 0.1%
22261
< 0.1%
22171
< 0.1%
22071
< 0.1%
21701
< 0.1%
21401
< 0.1%
21371
< 0.1%
21251
< 0.1%
21171
< 0.1%

Page Views Per Visit
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct114
Distinct (%)1.3%
Missing137
Missing (%)1.5%
Infinite0
Infinite (%)0.0%
Mean2.362819949
Minimum0
Maximum55
Zeros2189
Zeros (%)23.7%
Negative0
Negative (%)0.0%
Memory size72.3 KiB
2021-10-28T19:16:03.352715image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33
95-th percentile6
Maximum55
Range55
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.161417755
Coefficient of variation (CV)0.9147619373
Kurtosis42.36234824
Mean2.362819949
Median Absolute Deviation (MAD)1
Skewness2.871792897
Sum21508.75
Variance4.67172671
MonotonicityNot monotonic
2021-10-28T19:16:03.680815image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02189
23.7%
21795
19.4%
31196
12.9%
4896
9.7%
1651
 
7.0%
5517
 
5.6%
1.5306
 
3.3%
6244
 
2.6%
2.5241
 
2.6%
7133
 
1.4%
Other values (104)935
10.1%
(Missing)137
 
1.5%
ValueCountFrequency (%)
02189
23.7%
1651
 
7.0%
1.142
 
< 0.1%
1.171
 
< 0.1%
1.191
 
< 0.1%
1.25
 
0.1%
1.211
 
< 0.1%
1.222
 
< 0.1%
1.232
 
< 0.1%
1.2523
 
0.2%
ValueCountFrequency (%)
551
 
< 0.1%
241
 
< 0.1%
163
 
< 0.1%
154
< 0.1%
14.51
 
< 0.1%
149
0.1%
136
0.1%
12.331
 
< 0.1%
125
0.1%
11.51
 
< 0.1%

Last Activity
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct17
Distinct (%)0.2%
Missing103
Missing (%)1.1%
Memory size72.3 KiB
Email Opened
3437 
SMS Sent
2745 
Olark Chat Conversation
973 
Page Visited on Website
640 
Converted to Lead
428 
Other values (12)
914 

Length

Max length28
Median length12
Mean length13.40024078
Min length8

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowPage Visited on Website
2nd rowEmail Opened
3rd rowEmail Opened
4th rowUnreachable
5th rowConverted to Lead

Common Values

ValueCountFrequency (%)
Email Opened3437
37.2%
SMS Sent2745
29.7%
Olark Chat Conversation973
 
10.5%
Page Visited on Website640
 
6.9%
Converted to Lead428
 
4.6%
Email Bounced326
 
3.5%
Email Link Clicked267
 
2.9%
Form Submitted on Website116
 
1.3%
Unreachable93
 
1.0%
Unsubscribed61
 
0.7%
Other values (7)51
 
0.6%
(Missing)103
 
1.1%

Length

2021-10-28T19:16:03.962031image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
email4034
18.9%
opened3437
16.1%
sms2745
12.8%
sent2745
12.8%
conversation1003
 
4.7%
olark973
 
4.6%
chat973
 
4.6%
on756
 
3.5%
website756
 
3.5%
visited641
 
3.0%
Other values (26)3320
15.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Country
Categorical

HIGH CORRELATION
MISSING

Distinct38
Distinct (%)0.6%
Missing2461
Missing (%)26.6%
Memory size72.3 KiB
India
6492 
United States
 
69
United Arab Emirates
 
53
Singapore
 
24
Saudi Arabia
 
21
Other values (33)
 
120

Length

Max length20
Median length5
Mean length5.291930963
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)0.1%

Sample

1st rowIndia
2nd rowIndia
3rd rowIndia
4th rowIndia
5th rowIndia

Common Values

ValueCountFrequency (%)
India6492
70.3%
United States69
 
0.7%
United Arab Emirates53
 
0.6%
Singapore24
 
0.3%
Saudi Arabia21
 
0.2%
United Kingdom15
 
0.2%
Australia13
 
0.1%
Qatar10
 
0.1%
Bahrain7
 
0.1%
Hong Kong7
 
0.1%
Other values (28)68
 
0.7%
(Missing)2461
 
26.6%

Length

2021-10-28T19:16:04.258899image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
india6492
92.7%
united137
 
2.0%
states69
 
1.0%
arab53
 
0.8%
emirates53
 
0.8%
singapore24
 
0.3%
saudi21
 
0.3%
arabia21
 
0.3%
kingdom15
 
0.2%
australia13
 
0.2%
Other values (35)106
 
1.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Specialization
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct19
Distinct (%)0.2%
Missing1438
Missing (%)15.6%
Memory size72.3 KiB
Select
1942 
Finance Management
976 
Human Resource Management
848 
Marketing Management
838 
Operations Management
503 
Other values (14)
2695 

Length

Max length33
Median length20
Mean length17.64688541
Min length6

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSelect
2nd rowSelect
3rd rowBusiness Administration
4th rowMedia and Advertising
5th rowSelect

Common Values

ValueCountFrequency (%)
Select1942
21.0%
Finance Management976
10.6%
Human Resource Management848
9.2%
Marketing Management838
9.1%
Operations Management503
 
5.4%
Business Administration403
 
4.4%
IT Projects Management366
 
4.0%
Supply Chain Management349
 
3.8%
Banking, Investment And Insurance338
 
3.7%
Media and Advertising203
 
2.2%
Other values (9)1036
11.2%
(Missing)1438
15.6%

Length

2021-10-28T19:16:04.524488image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
management4253
26.2%
select1942
12.0%
finance976
 
6.0%
human848
 
5.2%
resource848
 
5.2%
marketing838
 
5.2%
and817
 
5.0%
business581
 
3.6%
operations503
 
3.1%
administration403
 
2.5%
Other values (21)4202
25.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

How did you hear about X Education
Categorical

HIGH CORRELATION
MISSING

Distinct10
Distinct (%)0.1%
Missing2207
Missing (%)23.9%
Memory size72.3 KiB
Select
5043 
Online Search
808 
Word Of Mouth
 
348
Student of SomeSchool
 
310
Other
 
186
Other values (5)
 
338

Length

Max length21
Median length6
Mean length8.124697853
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSelect
2nd rowSelect
3rd rowSelect
4th rowWord Of Mouth
5th rowOther

Common Values

ValueCountFrequency (%)
Select5043
54.6%
Online Search808
 
8.7%
Word Of Mouth348
 
3.8%
Student of SomeSchool310
 
3.4%
Other186
 
2.0%
Multiple Sources152
 
1.6%
Advertisements70
 
0.8%
Social Media67
 
0.7%
Email26
 
0.3%
SMS23
 
0.2%
(Missing)2207
23.9%

Length

2021-10-28T19:16:04.930727image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-28T19:16:05.113773image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
select5043
53.8%
online808
 
8.6%
search808
 
8.6%
of658
 
7.0%
word348
 
3.7%
mouth348
 
3.7%
student310
 
3.3%
someschool310
 
3.3%
other186
 
2.0%
multiple152
 
1.6%
Other values (6)405
 
4.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

What is your current occupation
Categorical

HIGH CORRELATION
MISSING

Distinct6
Distinct (%)0.1%
Missing2690
Missing (%)29.1%
Memory size72.3 KiB
Unemployed
5600 
Working Professional
706 
Student
 
210
Other
 
16
Housewife
 
10

Length

Max length20
Median length10
Mean length10.96916031
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUnemployed
2nd rowUnemployed
3rd rowStudent
4th rowUnemployed
5th rowUnemployed

Common Values

ValueCountFrequency (%)
Unemployed5600
60.6%
Working Professional706
 
7.6%
Student210
 
2.3%
Other16
 
0.2%
Housewife10
 
0.1%
Businessman8
 
0.1%
(Missing)2690
29.1%

Length

2021-10-28T19:16:05.508672image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-28T19:16:05.730655image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
unemployed5600
77.2%
working706
 
9.7%
professional706
 
9.7%
student210
 
2.9%
other16
 
0.2%
housewife10
 
0.1%
businessman8
 
0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

What matters most to you in choosing a course
Categorical

HIGH CORRELATION
MISSING

Distinct3
Distinct (%)< 0.1%
Missing2709
Missing (%)29.3%
Memory size72.3 KiB
Better Career Prospects
6528 
Flexibility & Convenience
 
2
Other
 
1

Length

Max length25
Median length23
Mean length22.99785638
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowBetter Career Prospects
2nd rowBetter Career Prospects
3rd rowBetter Career Prospects
4th rowBetter Career Prospects
5th rowBetter Career Prospects

Common Values

ValueCountFrequency (%)
Better Career Prospects6528
70.6%
Flexibility & Convenience2
 
< 0.1%
Other1
 
< 0.1%
(Missing)2709
29.3%

Length

2021-10-28T19:16:06.011907image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-28T19:16:06.183766image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
better6528
33.3%
career6528
33.3%
prospects6528
33.3%
flexibility2
 
< 0.1%
2
 
< 0.1%
convenience2
 
< 0.1%
other1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Search
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.1 KiB
False
9226 
True
 
14
ValueCountFrequency (%)
False9226
99.8%
True14
 
0.2%
2021-10-28T19:16:06.308739image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Magazine
Boolean

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.1 KiB
False
9240 
ValueCountFrequency (%)
False9240
100.0%
2021-10-28T19:16:06.386858image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Newspaper Article
Boolean

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.1 KiB
False
9238 
True
 
2
ValueCountFrequency (%)
False9238
> 99.9%
True2
 
< 0.1%
2021-10-28T19:16:06.464983image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

X Education Forums
Boolean

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.1 KiB
False
9239 
True
 
1
ValueCountFrequency (%)
False9239
> 99.9%
True1
 
< 0.1%
2021-10-28T19:16:06.589968image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Newspaper
Boolean

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.1 KiB
False
9239 
True
 
1
ValueCountFrequency (%)
False9239
> 99.9%
True1
 
< 0.1%
2021-10-28T19:16:06.699355image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Digital Advertisement
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.1 KiB
False
9236 
True
 
4
ValueCountFrequency (%)
False9236
> 99.9%
True4
 
< 0.1%
2021-10-28T19:16:06.808722image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Through Recommendations
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.1 KiB
False
9233 
True
 
7
ValueCountFrequency (%)
False9233
99.9%
True7
 
0.1%
2021-10-28T19:16:06.902465image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Receive More Updates About Our Courses
Boolean

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.1 KiB
False
9240 
ValueCountFrequency (%)
False9240
100.0%
2021-10-28T19:16:06.996192image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Tags
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct26
Distinct (%)0.4%
Missing3353
Missing (%)36.3%
Memory size72.3 KiB
Will revert after reading the email
2072 
Ringing
1203 
Interested in other courses
513 
Already a student
465 
Closed by Horizzon
358 
Other values (21)
1276 

Length

Max length49
Median length22
Mean length22.14540513
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowInterested in other courses
2nd rowRinging
3rd rowWill revert after reading the email
4th rowRinging
5th rowWill revert after reading the email

Common Values

ValueCountFrequency (%)
Will revert after reading the email2072
22.4%
Ringing1203
 
13.0%
Interested in other courses513
 
5.6%
Already a student465
 
5.0%
Closed by Horizzon358
 
3.9%
switched off240
 
2.6%
Busy186
 
2.0%
Lost to EINS175
 
1.9%
Not doing further education145
 
1.6%
Interested in full time MBA117
 
1.3%
Other values (16)413
 
4.5%
(Missing)3353
36.3%

Length

2021-10-28T19:16:07.152449image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
the2074
 
9.5%
will2072
 
9.5%
email2072
 
9.5%
revert2072
 
9.5%
reading2072
 
9.5%
after2072
 
9.5%
ringing1203
 
5.5%
in765
 
3.5%
interested635
 
2.9%
courses513
 
2.4%
Other values (61)6215
28.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Lead Quality
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct5
Distinct (%)0.1%
Missing4767
Missing (%)51.6%
Memory size72.3 KiB
Might be
1560 
Not Sure
1092 
High in Relevance
637 
Worst
601 
Low in Relevance
583 

Length

Max length17
Median length8
Mean length9.921305611
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLow in Relevance
2nd rowMight be
3rd rowNot Sure
4th rowMight be
5th rowLow in Relevance

Common Values

ValueCountFrequency (%)
Might be1560
 
16.9%
Not Sure1092
 
11.8%
High in Relevance637
 
6.9%
Worst601
 
6.5%
Low in Relevance583
 
6.3%
(Missing)4767
51.6%

Length

2021-10-28T19:16:07.402411image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-28T19:16:07.574274image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
might1560
16.3%
be1560
16.3%
in1220
12.8%
relevance1220
12.8%
not1092
11.4%
sure1092
11.4%
high637
6.7%
worst601
 
6.3%
low583
 
6.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Update me on Supply Chain Content
Boolean

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.1 KiB
False
9240 
ValueCountFrequency (%)
False9240
100.0%
2021-10-28T19:16:07.777385image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Get updates on DM Content
Boolean

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.1 KiB
False
9240 
ValueCountFrequency (%)
False9240
100.0%
2021-10-28T19:16:07.839880image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Lead Profile
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct6
Distinct (%)0.1%
Missing2709
Missing (%)29.3%
Memory size72.3 KiB
Select
4146 
Potential Lead
1613 
Other Leads
487 
Student of SomeSchool
 
241
Lateral Student
 
24

Length

Max length27
Median length6
Mean length8.999540652
Min length6

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSelect
2nd rowSelect
3rd rowPotential Lead
4th rowSelect
5th rowSelect

Common Values

ValueCountFrequency (%)
Select4146
44.9%
Potential Lead1613
 
17.5%
Other Leads487
 
5.3%
Student of SomeSchool241
 
2.6%
Lateral Student24
 
0.3%
Dual Specialization Student20
 
0.2%
(Missing)2709
29.3%

Length

2021-10-28T19:16:07.996122image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-28T19:16:08.152379image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
select4146
45.2%
potential1613
 
17.6%
lead1613
 
17.6%
other487
 
5.3%
leads487
 
5.3%
student285
 
3.1%
of241
 
2.6%
someschool241
 
2.6%
lateral24
 
0.3%
dual20
 
0.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

City
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct7
Distinct (%)0.1%
Missing1420
Missing (%)15.4%
Memory size72.3 KiB
Mumbai
3222 
Select
2249 
Thane & Outskirts
752 
Other Cities
686 
Other Cities of Maharashtra
457 
Other values (2)
454 

Length

Max length27
Median length6
Mean length9.470204604
Min length6

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSelect
2nd rowSelect
3rd rowMumbai
4th rowMumbai
5th rowMumbai

Common Values

ValueCountFrequency (%)
Mumbai3222
34.9%
Select2249
24.3%
Thane & Outskirts752
 
8.1%
Other Cities686
 
7.4%
Other Cities of Maharashtra457
 
4.9%
Other Metro Cities380
 
4.1%
Tier II Cities74
 
0.8%
(Missing)1420
15.4%

Length

2021-10-28T19:16:08.417983image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-28T19:16:08.589825image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
mumbai3222
26.2%
select2249
18.3%
cities1597
13.0%
other1523
12.4%
thane752
 
6.1%
752
 
6.1%
outskirts752
 
6.1%
of457
 
3.7%
maharashtra457
 
3.7%
metro380
 
3.1%
Other values (2)148
 
1.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Asymmetrique Activity Index
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct3
Distinct (%)0.1%
Missing4218
Missing (%)45.6%
Memory size72.3 KiB
02.Medium
3839 
01.High
821 
03.Low
 
362

Length

Max length9
Median length9
Mean length8.456790123
Min length6

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row02.Medium
2nd row02.Medium
3rd row02.Medium
4th row02.Medium
5th row02.Medium

Common Values

ValueCountFrequency (%)
02.Medium3839
41.5%
01.High821
 
8.9%
03.Low362
 
3.9%
(Missing)4218
45.6%

Length

2021-10-28T19:16:08.964804image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-28T19:16:09.152284image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
02.medium3839
76.4%
01.high821
 
16.3%
03.low362
 
7.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Asymmetrique Profile Index
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct3
Distinct (%)0.1%
Missing4218
Missing (%)45.6%
Memory size72.3 KiB
02.Medium
2788 
01.High
2203 
03.Low
 
31

Length

Max length9
Median length9
Mean length8.104141776
Min length6

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row02.Medium
2nd row02.Medium
3rd row01.High
4th row01.High
5th row01.High

Common Values

ValueCountFrequency (%)
02.Medium2788
30.2%
01.High2203
23.8%
03.Low31
 
0.3%
(Missing)4218
45.6%

Length

2021-10-28T19:16:09.371023image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-28T19:16:09.558505image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
02.medium2788
55.5%
01.high2203
43.9%
03.low31
 
0.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Asymmetrique Activity Score
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct12
Distinct (%)0.2%
Missing4218
Missing (%)45.6%
Infinite0
Infinite (%)0.0%
Mean14.30625249
Minimum7
Maximum18
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size72.3 KiB
2021-10-28T19:16:09.808490image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile12
Q114
median14
Q315
95-th percentile17
Maximum18
Range11
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.386694079
Coefficient of variation (CV)0.09692923286
Kurtosis1.233085585
Mean14.30625249
Median Absolute Deviation (MAD)1
Skewness-0.3833796985
Sum71846
Variance1.922920468
MonotonicityNot monotonic
2021-10-28T19:16:10.042846image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
141771
19.2%
151293
 
14.0%
13775
 
8.4%
16467
 
5.1%
17349
 
3.8%
12196
 
2.1%
1195
 
1.0%
1057
 
0.6%
99
 
0.1%
185
 
0.1%
Other values (2)5
 
0.1%
(Missing)4218
45.6%
ValueCountFrequency (%)
71
 
< 0.1%
84
 
< 0.1%
99
 
0.1%
1057
 
0.6%
1195
 
1.0%
12196
 
2.1%
13775
8.4%
141771
19.2%
151293
14.0%
16467
 
5.1%
ValueCountFrequency (%)
185
 
0.1%
17349
 
3.8%
16467
 
5.1%
151293
14.0%
141771
19.2%
13775
8.4%
12196
 
2.1%
1195
 
1.0%
1057
 
0.6%
99
 
0.1%

Asymmetrique Profile Score
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct10
Distinct (%)0.2%
Missing4218
Missing (%)45.6%
Infinite0
Infinite (%)0.0%
Mean16.34488252
Minimum11
Maximum20
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size72.3 KiB
2021-10-28T19:16:10.433465image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile14
Q115
median16
Q318
95-th percentile20
Maximum20
Range9
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.811395003
Coefficient of variation (CV)0.1108233725
Kurtosis-0.6197314458
Mean16.34488252
Median Absolute Deviation (MAD)1
Skewness0.2217387181
Sum82084
Variance3.281151858
MonotonicityNot monotonic
2021-10-28T19:16:10.620931image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
151759
19.0%
181071
 
11.6%
16599
 
6.5%
17579
 
6.3%
20308
 
3.3%
19245
 
2.7%
14226
 
2.4%
13204
 
2.2%
1222
 
0.2%
119
 
0.1%
(Missing)4218
45.6%
ValueCountFrequency (%)
119
 
0.1%
1222
 
0.2%
13204
 
2.2%
14226
 
2.4%
151759
19.0%
16599
 
6.5%
17579
 
6.3%
181071
11.6%
19245
 
2.7%
20308
 
3.3%
ValueCountFrequency (%)
20308
 
3.3%
19245
 
2.7%
181071
11.6%
17579
 
6.3%
16599
 
6.5%
151759
19.0%
14226
 
2.4%
13204
 
2.2%
1222
 
0.2%
119
 
0.1%

I agree to pay the amount through cheque
Boolean

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.1 KiB
False
9240 
ValueCountFrequency (%)
False9240
100.0%
2021-10-28T19:16:10.824044image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

A free copy of Mastering The Interview
Boolean

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.1 KiB
False
6352 
True
2888 
ValueCountFrequency (%)
False6352
68.7%
True2888
31.3%
2021-10-28T19:16:10.902181image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Last Notable Activity
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct16
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size72.3 KiB
Modified
3407 
Email Opened
2827 
SMS Sent
2172 
Page Visited on Website
 
318
Olark Chat Conversation
 
183
Other values (11)
 
333

Length

Max length28
Median length8
Mean length10.32099567
Min length8

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)0.1%

Sample

1st rowModified
2nd rowEmail Opened
3rd rowEmail Opened
4th rowModified
5th rowModified

Common Values

ValueCountFrequency (%)
Modified3407
36.9%
Email Opened2827
30.6%
SMS Sent2172
23.5%
Page Visited on Website318
 
3.4%
Olark Chat Conversation183
 
2.0%
Email Link Clicked173
 
1.9%
Email Bounced60
 
0.6%
Unsubscribed47
 
0.5%
Unreachable32
 
0.3%
Had a Phone Conversation14
 
0.2%
Other values (6)7
 
0.1%

Length

2021-10-28T19:16:11.089664image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
modified3407
21.3%
email3063
19.1%
opened2827
17.6%
sms2172
13.6%
sent2172
13.6%
on319
 
2.0%
website319
 
2.0%
page318
 
2.0%
visited318
 
2.0%
conversation197
 
1.2%
Other values (23)910
 
5.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Interactions

2021-10-28T19:15:50.543054image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:15:40.846704image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:15:43.011306image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:15:44.864064image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:15:46.752438image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:15:48.704423image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:15:50.863612image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:15:41.346675image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:15:43.374097image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:15:45.254940image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:15:47.130182image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:15:49.058418image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:15:51.135555image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:15:41.671203image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:15:43.648093image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:15:45.541773image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:15:47.550036image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:15:49.351220image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:15:51.382866image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:15:42.018669image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:15:43.935359image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:15:45.835603image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:15:47.824402image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:15:49.680068image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:15:51.639881image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:15:42.366382image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:15:44.296175image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:15:46.126221image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:15:48.126937image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:15:49.978524image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:15:51.894675image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:15:42.686818image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:15:44.588470image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:15:46.384633image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:15:48.402333image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-28T19:15:50.240376image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2021-10-28T19:16:11.308402image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-10-28T19:16:11.683357image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-10-28T19:16:12.120827image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-10-28T19:16:12.573937image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2021-10-28T19:16:13.558240image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-10-28T19:15:52.500205image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2021-10-28T19:15:56.748173image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-10-28T19:15:57.891828image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-10-28T19:15:59.080870image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

Prospect IDLead NumberLead OriginLead SourceDo Not EmailDo Not CallConvertedTotalVisitsTotal Time Spent on WebsitePage Views Per VisitLast ActivityCountrySpecializationHow did you hear about X EducationWhat is your current occupationWhat matters most to you in choosing a courseSearchMagazineNewspaper ArticleX Education ForumsNewspaperDigital AdvertisementThrough RecommendationsReceive More Updates About Our CoursesTagsLead QualityUpdate me on Supply Chain ContentGet updates on DM ContentLead ProfileCityAsymmetrique Activity IndexAsymmetrique Profile IndexAsymmetrique Activity ScoreAsymmetrique Profile ScoreI agree to pay the amount through chequeA free copy of Mastering The InterviewLast Notable Activity
07927b2df-8bba-4d29-b9a2-b6e0beafe620660737APIOlark ChatNoNo00.000.0Page Visited on WebsiteNaNSelectSelectUnemployedBetter Career ProspectsNoNoNoNoNoNoNoNoInterested in other coursesLow in RelevanceNoNoSelectSelect02.Medium02.Medium15.015.0NoNoModified
12a272436-5132-4136-86fa-dcc88c88f482660728APIOrganic SearchNoNo05.06742.5Email OpenedIndiaSelectSelectUnemployedBetter Career ProspectsNoNoNoNoNoNoNoNoRingingNaNNoNoSelectSelect02.Medium02.Medium15.015.0NoNoEmail Opened
28cc8c611-a219-4f35-ad23-fdfd2656bd8a660727Landing Page SubmissionDirect TrafficNoNo12.015322.0Email OpenedIndiaBusiness AdministrationSelectStudentBetter Career ProspectsNoNoNoNoNoNoNoNoWill revert after reading the emailMight beNoNoPotential LeadMumbai02.Medium01.High14.020.0NoYesEmail Opened
30cc2df48-7cf4-4e39-9de9-19797f9b38cc660719Landing Page SubmissionDirect TrafficNoNo01.03051.0UnreachableIndiaMedia and AdvertisingWord Of MouthUnemployedBetter Career ProspectsNoNoNoNoNoNoNoNoRingingNot SureNoNoSelectMumbai02.Medium01.High13.017.0NoNoModified
43256f628-e534-4826-9d63-4a8b88782852660681Landing Page SubmissionGoogleNoNo12.014281.0Converted to LeadIndiaSelectOtherUnemployedBetter Career ProspectsNoNoNoNoNoNoNoNoWill revert after reading the emailMight beNoNoSelectMumbai02.Medium01.High15.018.0NoNoModified
52058ef08-2858-443e-a01f-a9237db2f5ce660680APIOlark ChatNoNo00.000.0Olark Chat ConversationNaNNaNNaNNaNNaNNoNoNoNoNoNoNoNoNaNNaNNoNoNaNNaN01.High02.Medium17.015.0NoNoModified
69fae7df4-169d-489b-afe4-0f3d752542ed660673Landing Page SubmissionGoogleNoNo12.016402.0Email OpenedIndiaSupply Chain ManagementOnline SearchUnemployedBetter Career ProspectsNoNoNoNoNoNoNoNoWill revert after reading the emailLow in RelevanceNoNoPotential LeadMumbai02.Medium01.High14.020.0NoNoModified
720ef72a2-fb3b-45e0-924e-551c5fa59095660664APIOlark ChatNoNo00.000.0Olark Chat ConversationNaNNaNNaNNaNNaNNoNoNoNoNoNoNoNoNaNNaNNoNoNaNNaN02.Medium02.Medium15.015.0NoNoModified
8cfa0128c-a0da-4656-9d47-0aa4e67bf690660624Landing Page SubmissionDirect TrafficNoNo02.0712.0Email OpenedIndiaIT Projects ManagementNaNNaNNaNNoNoNoNoNoNoNoNoNaNNaNNoNoNaNThane & Outskirts02.Medium02.Medium14.014.0NoYesEmail Opened
9af465dfc-7204-4130-9e05-33231863c4b5660616APIGoogleNoNo04.0584.0Email OpenedIndiaFinance ManagementWord Of MouthNaNNaNNoNoNoNoNoNoNoNoNaNNaNNoNoNaNMumbai02.Medium02.Medium13.016.0NoNoEmail Opened

Last rows

Prospect IDLead NumberLead OriginLead SourceDo Not EmailDo Not CallConvertedTotalVisitsTotal Time Spent on WebsitePage Views Per VisitLast ActivityCountrySpecializationHow did you hear about X EducationWhat is your current occupationWhat matters most to you in choosing a courseSearchMagazineNewspaper ArticleX Education ForumsNewspaperDigital AdvertisementThrough RecommendationsReceive More Updates About Our CoursesTagsLead QualityUpdate me on Supply Chain ContentGet updates on DM ContentLead ProfileCityAsymmetrique Activity IndexAsymmetrique Profile IndexAsymmetrique Activity ScoreAsymmetrique Profile ScoreI agree to pay the amount through chequeA free copy of Mastering The InterviewLast Notable Activity
9230d11c15b7-8056-45a6-8954-771c0d0495fe579701Landing Page SubmissionGoogleNoNo02.08702.00Email OpenedIndiaHuman Resource ManagementOnline SearchUnemployedBetter Career ProspectsNoNoNoNoNoNoNoNoWill revert after reading the emailNaNNoNoPotential LeadMumbai02.Medium01.High13.020.0NoNoEmail Opened
92314aeae36b-2b57-494f-bdab-dd58844286b4579697Landing Page SubmissionGoogleNoNo18.010164.00Email OpenedIndiaBanking, Investment And InsuranceOnline SearchUnemployedBetter Career ProspectsNoNoNoNoNoNoNoNoWill revert after reading the emailHigh in RelevanceNoNoPotential LeadMumbai02.Medium01.High15.020.0NoNoEmail Opened
92322d0109e9-dfb2-4664-83de-c2ea75ec7516579642Landing Page SubmissionDirect TrafficNoNo02.017702.00SMS SentIndiaHuman Resource ManagementSelectUnemployedBetter Career ProspectsNoNoNoNoNoNoNoNoRingingNot SureNoNoPotential LeadMumbai02.Medium01.High14.020.0NoYesSMS Sent
92333f715465-2546-47cd-afa8-8b8dc63b8b43579622APIDirect TrafficNoNo113.014092.60SMS SentIndiaSelectSelectUnemployedBetter Career ProspectsNoNoNoNoNoNoNoNoWill revert after reading the emailNaNNoNoSelectSelectNaNNaNNaNNaNNoNoSMS Sent
9234c0b25922-511f-4c56-852e-ced210a45447579615Landing Page SubmissionDirect TrafficNoNo15.02102.50SMS SentIndiaBusiness AdministrationSelectUnemployedBetter Career ProspectsNoNoNoNoNoNoNoNoWill revert after reading the emailMight beNoNoPotential LeadMumbai02.Medium01.High14.020.0NoNoModified
923519d6451e-fcd6-407c-b83b-48e1af805ea9579564Landing Page SubmissionDirect TrafficYesNo18.018452.67Email Marked SpamSaudi ArabiaIT Projects ManagementSelectUnemployedBetter Career ProspectsNoNoNoNoNoNoNoNoWill revert after reading the emailHigh in RelevanceNoNoPotential LeadMumbai02.Medium01.High15.017.0NoNoEmail Marked Spam
923682a7005b-7196-4d56-95ce-a79f937a158d579546Landing Page SubmissionDirect TrafficNoNo02.02382.00SMS SentIndiaMedia and AdvertisingSelectUnemployedBetter Career ProspectsNoNoNoNoNoNoNoNowrong number givenMight beNoNoPotential LeadMumbai02.Medium01.High14.019.0NoYesSMS Sent
9237aac550fe-a586-452d-8d3c-f1b62c94e02c579545Landing Page SubmissionDirect TrafficYesNo02.01992.00SMS SentIndiaBusiness AdministrationSelectUnemployedBetter Career ProspectsNoNoNoNoNoNoNoNoinvalid numberNot SureNoNoPotential LeadMumbai02.Medium01.High13.020.0NoYesSMS Sent
92385330a7d1-2f2b-4df4-85d6-64ca2f6b95b9579538Landing Page SubmissionGoogleNoNo13.04993.00SMS SentIndiaHuman Resource ManagementOnline SearchNaNNaNNoNoNoNoNoNoNoNoNaNNaNNoNoNaNOther Metro Cities02.Medium02.Medium15.016.0NoNoSMS Sent
9239571b5c8e-a5b2-4d57-8574-f2ffb06fdeff579533Landing Page SubmissionDirect TrafficNoNo16.012793.00SMS SentBangladeshSupply Chain ManagementSelectUnemployedBetter Career ProspectsNoNoNoNoNoNoNoNoWill revert after reading the emailMight beNoNoPotential LeadOther Cities02.Medium01.High15.018.0NoYesModified